Plasma processing systems have long been employed to process substrates such as semiconductor wafers and flat panels, for example. A plasma processing system may be employed to perform processes such as deposition, etching, cleaning, etc.
In a plasma processing system employed for producing semiconductor devices, for example, it is highly desirable that the plasma processing system produces electronic devices with the highest yield and with the lowest cost of ownership possible. To achieve a high yield and to reduce tool down time, which contributes to a higher cost of ownership, it is critical to detect and classify faults rapidly in order to minimize damage to wafers and/or to the plasma processing system components. A fault condition may arise due to, for example, chamber component malfunction, chamber component wear, incorrectly installed chamber components, and/or any other condition that requires cleaning, maintenance, and/or replacement of one or more subsystems of the plasma processing system.
A modern plasma processing system may employ numerous sensors to monitor various process parameters such as optical emission, voltage, current, pressure, temperature, etc. The data monitoring performed by each sensor may output data at rates of up to hundreds of samples per second or more. Given the large number of sensors involved, a modern plasma processing system may generate a huge volume of sensor data for a given processed wafer. If the analysis of the sensor data is performed manually, it is often impossible to accurately detect and/or classify a fault condition from the voluminous sensor data in a timely manner. If a fault condition is not detected in a timely manner, further processing may result in damage to one or more wafers and/or to chamber components. Even after plasma processing is halted, a large amount of time must be devoted to sifting through the voluminous sensor data to ascertain the fault that occurred in order to facilitate fault remedy.
Manual fault detection and analysis also requires highly skilled engineers to sift through the highly voluminous data. These highly skilled engineers are both in short supply and costly to employ, both of which increase the cost of ownership for the tool owner. The manual process of fault detection and analysis is also error-prone.
There have been attempts in the past to automatically detect fault conditions and to analyze the sensor data to classify faults. These efforts have met varying degrees of success in the production environment and in the marketplace. Engineers are constantly searching for ways to detect fault conditions more quickly and accurately classify faults. This application relates to improved methods and apparatus for automatically detecting fault conditions and for classifying fault conditions in an automatic and timely manner.